Zhao, J, Parry, CJ, dos Anjos, R et al. (2 more authors) (2020) Voice Interaction for Augmented Reality Navigation Interfaces with Natural Language Understanding. In: 35th International Conference on Image and Vision Computing New Zealand (IVCNZ). 35th International Conference on Image and Vision Computing New Zealand, 25-27 November 2020, Wellington, New Zealand. IEEE ISBN 978-1-7281-8580-4
Abstract
Voice interaction with natural language understanding (NLU) has been extensively explored in desktop computers, handheld devices, and human-robot interaction. However, there is limited research into voice interaction with NLU in augmented reality (AR). There are benefits of using voice interaction in AR, such as high naturalness and being hands-free. In this project, we introduce VOARLA, an NLU-powered AR voice interface, which navigate courier driver delivery a package. A user study was completed to evaluate VOARLA against an AR voice interface without NLU to investigate the effectiveness of NLU in the navigation interface in AR. We evaluated from three aspects: accuracy, productivity, and commands learning curve. Results found that using NLU in AR increases the accuracy of the interface by 15%. However, higher accuracy did not correlate to an increase in productivity. Results suggest that NLU helped users remember the commands on the first run when they were unfamiliar with the system. This suggests that using NLU in an AR hands-free application can make the learning curve easier for new users.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Augmented Reality; speech recognition; natural language understanding (NLU); speech interaction; artificial intelligence; intelligent interface |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 May 2024 13:15 |
Last Modified: | 07 May 2024 13:15 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ivcnz51579.2020.9290643 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194573 |